Instructions to use lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8")
model = AutoModelForCausalLM.from_pretrained("lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8

SGLang

How to use lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8 with Docker Model Runner:
```
docker model run hf.co/lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
```

Llama 3.1 8B Instruct - W8A8 Quantized

This is a W8A8 (8-bit weights and 8-bit activations) quantized version of meta-llama/Llama-3.1-8B-Instruct.

Model Description

Base Model: meta-llama/Llama-3.1-8B-Instruct
Quantization: W8A8 (8-bit weights, 8-bit activations)
Quantization Method: GPTQ using llmcompressor
Model Size: ~8GB (reduced from ~16GB FP16)

Quantization Details

This model was quantized using llmcompressor with the following configuration:

Calibration Dataset: HuggingFaceH4/ultrachat_200k (128 samples)
Max Sequence Length: 128
Scheme: W8A8
Targets: Linear layers (excluding lm_head)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Example inference
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)

response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)

Performance

Memory Usage: ~8GB GPU memory
Inference Speed: Faster than FP16 with minimal accuracy loss
Accuracy: Comparable to the original model on most tasks

License

This model inherits the Llama 3.1 Community License from the base model. Please refer to the original model card for full license details.

Citation

If you use this model, please cite both the original Llama 3.1 paper and the quantization library:

@article{llama3.1,
  title={The Llama 3 Herd of Models},
  author={Meta AI},
  year={2024},
  url={https://arxiv.org/abs/2407.21783}
}

Acknowledgments

Base model by Meta AI
Quantization using llmcompressor
Calibration dataset by HuggingFace H4 team

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(908)

this model

Paper for lsm0729/Meta-Llama-3.1-8B-Instruct-quantized.w8a8

The Llama 3 Herd of Models

Paper • 2407.21783 • Published Jul 31, 2024 • 119